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Medical motion plays an essential role in clinical treatment. The deep neural network is an emerging machine learning method that has proven its potential for different classification tasks. Notably, the convolutional neural network dominates with the best results on varying image classification tasks. However, medical image datasets are hard to collect because it needs a lot of professional expertise to label them. Although it can be detected and treated with very less sophisticated instruments and medication. Therefore, this paper how to apply the Convolutional Neural Network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia. The objective and automated detection of pneumonia represents a serious challenge in medical imaging because the signs of the illness are not obvious in CT or X-ray scans. Further on, it is also an important task, since millions of people die of pneumonia every year. Deep learning-based methods have shown good generalization traits over various problem domains, which prompts researchers around the globe to work tirelessly and come up with more efficient and effective models than earlier. However, this robust nature comes at the cost of high computational resources and, in general, it requires a huge amount of data to train the model efficiently. The latter requirement sometimes cannot be fulfilled, especially in the biomedical field. The main goal is to propose a solution for the above-mentioned problem, using a novel deep neural network architecture. The proposed novelty consists of the use of dropout in the convolutional part of the network. The proposed method was trained and tested on a set of labeled images. This project aims to introduce a deep learning technology based on the computational neural network, which can realize automatic diagnosis of patients with pneumonia in X-ray images. |
